Digital Media Processing: DSP Algorithms Using C

Digital Media Processing: DSP Algorithms Using C

Hazarathaiah Malepati

Language: English

Pages: 768

ISBN: 1856176789

Format: PDF / Kindle (mobi) / ePub

Multimedia processing demands efficient programming in order to optimize functionality. Data, image, audio, and video processing, some or all of which are present in all electronic devices today, are complex programming environments. Optimized algorithms (step-by-step directions) are difficult to create but can make all the difference when developing a new application.

This book discusses the most current algorithms available that will maximize your programming keeping in mind the memory and real-time constraints of the architecture with which you are working. A wide range of algorithms is covered detailing basic and advanced multimedia implementations, along with, cryptography, compression, and data error correction. The general implementation concepts can be integrated into many architectures that you find yourself working with on a specific project. Analog Devices' BlackFin technology is used for examples throughout the book.

*Discusses how to decrease algorithm development times to streamline your programming
*Covers all the latest algorithms needed for contrained systems
*Includes case studies on WiMAX, GPS, and portable media players

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Eliminate pipeline stalls of the reference embedded processor. If we do not interleave the program code, then the cycle consumption increases by a lot as the approach for implementation of RS codes involves many data load/store memory accesses. In addition, we did not include the overhead of initialization of variables, jumps and other pipeline stalls in obtaining cycle consumption expressions. RS Encoder Computational Complexity Based on Section 4.2.4, the cycles estimate for the RS(N, K ).

Hamming (72, 64) coder, 64 bits of data enter into the encoder block at point A and 72 bits of encoded data come out at point B. Then this encoded 72-bit data frame is stored to the memory. This process will be continued for all data blocks. 3.4.3 Hamming (72, 64) Decoder The Hamming decoder consists of two steps: (1) syndrome computation and (2) error correction. In the syndrome computation step, the Hamming (72, 64) decoder computes eight syndromes using the 72 bits retrieved from memory. The.

The least probable path accumulated state metric). The solid lines represent the most probable paths to a current state from a previous state with minimum state metric (when compared to other state metrics). Next, we discuss a few specific cases that arise in trellis processing. At stage i = 2, we have two state metrics with the same accumulated metric values and those two paths diverge from the same previous state. In this case, as we don’t know in advance which path is going to survive, we.

M 0,m f (0,m) m αn γn βn+1 δ¯n = LLR(cn ) = ln f (1,m) = ln αnm γn1,m βn+1 (3.52) f (0,m) − ln αnm γn0,m βn+1 m m ¯ b¯ ¯ then ab = ea+ If ln(ab) = ln(a) + ln(b) = a¯ + b, . Using this transformation, f (1,m) δ¯n = ln α¯ nm +γ¯n1,m +β¯n+1 e f (0,m) − ln e m α¯ nm +γ¯n0,m +β¯n+1 (3.53) m f (1,m) f (1,m) where α¯ nm , β¯n+1 , γ¯n1,m and δ¯n are logarithms of αnm , βn+1 , γn1,m and δn , respectively. Forward Metric Computation The forward state metrics α¯ nm are recursively.

Algorithms, Gallager (1963) and MacKay (1999) are recommended. We use the following notations for the sum-product algorithm: Q i j = Extrinsic information to be passed from bit node i to parity node j R j i = Extrinsic information to be passed from parity node j to bit node i Ui = { j such that h j i = 1}, the set of row locations of the 1s in the i-th column Introduction to Data Error Correction b1 b4 b3 b6 Q13 Q33 Q43 b5 Q63 R 05 Figure 3.55: (a) Connections to third parity node from.

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